Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects millions of individuals in the United States alone. Common symptoms of the disease are forgetfulness and memory loss. However, these AD symptoms typically appear later in life despite potential early and hidden biological changes in the brain. This preclinical stage can began years before the onset of the typical symptoms of AD marking the need for earlier detection methods for developing therapies to slow symptom progression. Here, I have developed an initial susceptibility weighted imaging (SWI) method, a subset of magnetic resonance imaging, for the purpose of longitudinal study of AD.

This thesis aims to determine whether people with Parkinson's disease differ from people with multiple sclerosis in their responsivity to reactive balance training, which is vital for reducing the risk of falls, as measured by the amount of time it takes for postural muscle activation after the addition of a perturbation stimulus.

It is hypothesized that changes in brain tissue microstructure, particularly degradation of neurites (i.e,. axons and dendrites) and synapses, are early drivers of Alzheimer's disease (AD) pathogenesis. Quantitative magnetic resonance imaging (MRI) tools like diffusion tensor imaging (DTI) have long been used to study AD pathogenesis. Using DTI metrics, structural insights of neuro tissue can be inferred but not directly measured. DTI has proven to be an effective tool indicating fractional anisotrophy (FA) differences between groups of varying AD risk factor, but it does not explicitly quantify pathophysiologically-relevant features like neurite density and complexity. This study aims to develop and validate an advanced diffusion MRI acquisition and biophysical modeling platform that can be used to explicitly quantify changes to brain tissue microstructure, specifically neurite density and complexity. Ultimately, this platform will be used to study the pathogenic mechanisms that drive AD in the pre-clinical and clinical setting.

A current thrust in neurorehabilitation research involves exogenous neuromodulation of peripheral nerves to enhance neuroplasticity and maximize recovery of function. This dissertation presents the results of four experiments aimed at assessing the effects of trigeminal nerve stimulation (TNS) and occipital nerve stimulation (ONS) on motor learning, which was behaviorally characterized using an upper extremity visuomotor adaptation paradigm. In Aim 1a, the effects of offline TNS using clinically tested frequencies (120 and 60 Hz) were characterized. Sixty-three participants (22.75±4.6 y/o), performed a visuomotor rotation task and received TNS before encountering rotation of hand visual feedback. In Aim 1b, TNS at 3 kHz, which has been shown to be more tolerable at higher current intensities, was evaluated in 42 additional subjects (23.4±4.6 y/o). Results indicated that 3 kHz stimulation accelerated learning while 60 Hz stimulation slowed learning, suggesting a frequency-dependent effect on learning. In Aim 2, the effect of online TNS using 120 and 60 Hz were characterized to determine if this protocol would deliver better outcomes. Sixty-three participants (23.2±3.9 y/o) received either TNS or sham concurrently with perturbed visual feedback. Results showed no significant differences among groups. However, a cross-study comparison of results obtained with 60 Hz offline TNS showed a statistically significant improvement in learning rates with online stimulation relative to offline, suggesting a timing-dependent effect on learning. In Aim 3, TNS and ONS were compared using the best protocol from previous aims (offline 3 kHz). Additionally, concurrent stimulation of both nerves was explored to look for potential synergistic effects. Eighty-four participants (22.9±3.2 y/o) were assigned to one of four groups: TNS, ONS, TNS+ONS, and sham. Visual inspection of learning curves revealed that the ONS group demonstrated the fastest learning among groups. However, statistical analyses did not confirm this observation. In addition, the TNS+ONS group appeared to learn faster than the sham and TNS groups but slower than the ONS only group, suggesting no synergistic effects using this protocol, as initially hypothesized. The results provide new information on the potential use of TNS and ONS in neurorehabilitation and performance enhancement in the motor domain.

The corpus callosum is a core white matter structure that sits at the center of the brain, playing a role in both interhemispheric communication and the inhibition of hemispheric activity to promote lateralization. Structural connectivity is thought to underlie functional connectivity (FC), but cases of structural brain abnormalities allow for a better understanding of this relationship. Agenesis of the corpus callosum (AgCC) is a condition in which an individual is born without a corpus callosum. These individuals provide a unique opportunity to investigate ways in which the brain adapts its functional organization to the lack of interhemispheric structural connectivity, thereby providing unique insights into brain network organization within and between the two cerebral hemispheres. The present study uses resting-state functional magnetic resonance imaging (fMRI) to compare the network connectivity of an individual with AgCC without any significant comorbidities to a control group of neurotypical adults (n=30). Potential differences of FC within the default mode network and frontoparietal network, as well as FC between these networks and bilateral language networks were examined. The AgCC individual displayed significantly higher FC within the frontoparietal network (t(29)=1.84, p<0.05) and significantly lower FC between the default mode network and the right ventral language stream (t(29)=-1.81, p<0.05) compared to the control group. Further analyses suggest that the right hemisphere’s frontoparietal network is driving the significant difference between the case study and control group in the frontoparietal network. The stronger FC of the frontoparietal network may represent a compensatory strategy used to support lower overall levels of default mode network and dual stream language network connectivity. Overall, the findings suggest that decreased interhemispheric structural connectivity may lead to increased compensation via attention networks such as the frontoparietal network, and decreased right hemisphere language network involvement.

Stroke is the leading cause of long-term disability in the U.S., with up to 60% of strokescausing speech loss. Individuals with severe stroke, who require the most frequent, intense speech therapy, often cannot adhere to treatments due to high cost and low success rates. Therefore, the ability to make functionally significant changes in individuals with severe post- stroke aphasia remains a key challenge for the rehabilitation community. This dissertation aimed to evaluate the efficacy of Startle Adjuvant Rehabilitation Therapy (START), a tele-enabled, low- cost treatment, to improve quality of life and speech in individuals with severe-to-moderate stroke. START is the exposure to startling acoustic stimuli during practice of motor tasks in individuals with stroke. START increases the speed and intensity of practice in severely impaired post-stroke reaching, with START eliciting muscle activity 2-3 times higher than maximum voluntary contraction. Voluntary reaching distance, onset, and final accuracy increased after a session of START, suggesting a rehabilitative effect. However, START has not been evaluated during impaired speech. The objective of this study is to determine if impaired speech can be elicited by startling acoustic stimuli, and if three days of START training can enhance clinical measures of moderate to severe post-stroke aphasia and apraxia of speech. This dissertation evaluates START in 42 individuals with post-stroke speech impairment via telehealth in a Phase 0 clinical trial. Results suggest that impaired speech can be elicited by startling acoustic stimuli and that START benefits individuals with severe-to-moderate post-stroke impairments in both linguistic and motor speech domains. This fills an important gap in aphasia care, as many speech therapies remain ineffective and financially inaccessible for patients with severe deficits. START is effective, remotely delivered, and may likely serve as an affordable adjuvant to traditional therapy for those that have poor access to quality care.

Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance. For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.